Dynamic selection of groups of outbound marketing events

ABSTRACT

A database system and method for ordering marketing events for offering to candidates. The database system comprises a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a database manager software application stored on a computer readable medium. The database manager software application comprises a grouping tool and an optimization tool. The marketing events from the first list are divided into a first plurality of groups. The grouping tool is for dividing candidates from the second list into a second plurality of groups and matching a first group from first plurality of groups with a second group from the second plurality of groups. The optimization tool is for optimizing and sorting the marketing events from the first group for all candidates from the second group.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to a system and associated method forordering outbound marketing events for offering to groups of candidates.

2. Related Art

Selling a plurality of services to customers typically requires acomplicated series of steps. Therefore there exists a need for a simpleprocedure to sell a plurality of services to customers.

SUMMARY OF THE INVENTION

The present invention provides database system, comprising:

a first database structure storing a first list identifying marketingevents, wherein each marketing event from said first list comprises amarketing offer and an identified channel means for communicating saidmarketing offer, and wherein said marketing events from said first listare divided into a first plurality of groups;

a second database structure storing a second list of candidates; and

a database manager software application stored on a computer readablemedium, wherein said database manager software application comprises agrouping tool and an optimization tool, wherein said grouping tool isfor dividing candidates from said second list of candidates into asecond plurality of groups and matching a first group from said firstplurality of groups with a second group from said second plurality ofgroups, wherein candidates from said second group comprise a firstspecified candidate trait, and wherein said optimization tool is foroptimizing and sorting, said marketing events from said first group forall candidates from said second group.

The present invention provides a selection method, comprising:

providing a database system comprising a first database structurestoring a first list identifying marketing events, a second databasestructure storing a second list of candidates, and a database managersoftware application stored on a computer readable medium, wherein saiddatabase manager software application comprises a grouping tool and anoptimization tool, wherein each marketing event from said first listcomprises a marketing offer and an identified channel means forcommunicating said marketing offer, and wherein said marketing eventsfrom said first list are divided into a first plurality of groups;

dividing by said grouping tool, candidates from said second list ofcandidates into a second plurality of groups;

matching by said grouping tool, a first group from said first pluralityof groups with a second group from said second plurality of groups,wherein all candidates from said second group comprise a first specifiedcandidate trait;

optimizing, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group; and

sorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.

The present invention provides a process for integrating computinginfrastructure, comprising integrating computer-readable code into acomputing system, wherein the code in combination with the computingsystem comprises a database system comprising a first database structurestoring a first list identifying marketing events, a second databasestructure storing a second list of candidates, and a database managersoftware application stored on a computer readable medium, wherein saiddatabase manager software application comprises a grouping tool and anoptimization tool, wherein each marketing event from said first listcomprises a marketing offer and an identified channel means forcommunicating said marketing offer, wherein said marketing events fromsaid first list are divided into a first plurality of groups, andwherein the code in combination with the computing system is adapted toimplement a method for performing the steps of:

dividing by said grouping tool, candidates from said second list ofcandidates into a second plurality of groups;

matching by said grouping tool, a first group from said first pluralityof groups with a second group from said second plurality of groups,wherein all candidates from said second group comprise a first specifiedcandidate trait;

optimizing, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group; and

sorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.

The present invention provides computer program product, comprising acomputer usable medium having a computer readable program code embodiedtherein, said computer readable program code comprising an algorithmadapted to implement a method for ordering a first list identifying ofmarketing events within a database system, said database systemcomprising a database system comprising a first database structurestoring said first list identifying marketing events, a second databasestructure storing a second list of candidates, and a database managersoftware application stored on a computer readable medium, wherein saiddatabase manager software application comprises a grouping tool and anoptimization tool, wherein each marketing event from said first listcomprises a marketing offer and an identified channel means forcommunicating said marketing offer, wherein said marketing events fromsaid first list are divided into a first plurality of groups, saidmethod comprising the steps of:

dividing by said grouping tool, candidates from said second list ofcandidates into a second plurality of groups;

matching by said grouping tool, a first group from said first pluralityof groups with a second group from said second plurality of groups,wherein all candidates from said second group comprise a first specifiedcandidate trait;

optimizing, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group; and

sorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.

The present invention advantageously provides a system and associatedmethod to implement a simple procedure to sell a plurality of servicesto customers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram view of a database system fordynamically ordering a plurality of marketing events for offering togroups of candidates, in accordance with embodiments of the presentinvention.

FIG. 2 illustrates a flowchart comprising an algorithm used by databasesystem of FIG. 1 for dynamically ordering a plurality of marketingevents for offering to groups of candidates, in accordance withembodiments of the present invention.

FIG. 3 illustrates a computer system used for implementing the databasesystem for dynamically ordering a plurality of marketing events foroffering to groups of candidates, in accordance with embodiments of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a block diagram view of a database system 2 fordynamically ordering a list identifying a plurality of marketing eventsfor offering marketing offers to a group of candidates, in accordancewith embodiments of the present invention. Each marketing eventcomprises a marketing offer and an identified channel means forcommunicating the marketing offers to a candidate. For example, amarketing event may comprise a 1.9% APR introductory interest rate for a12 month period on a credit card, delivered to the candidate throughdirect mail. A marketing event may either comprise a growth (newmarketing offer) marketing event or a retention (existing product likean existing credit card to be retained) marketing event. A marketingoffer is defined herein as an offer from an entity (e.g., a business)for a product or service to be extended to a customer or potentialcustomer (i.e.,.candidate). The product or service may comprise anincentive to entice the customer or potential customer to accept theoffer. For example, the marketing offer may comprise a credit card offerthat will provide an APR that is lower than the normal for a specifiedperiod of time as an incentive, such as, inter alia, 1.9% APR for thefirst 12 months if the credit card is accepted. A candidate may be anexisting customer of the entity and a potential customer for themarketing offer. For example, an individual, a business, etc. A channelis a means to contact the candidate. For example, e-mail, direct mail,text message, telephone, etc. An outbound marketing event comprises amarketing event where an initial contact is made by an entity to offerthe marketing event to a candidate. The database system 2 is used by anentity (e.g., a business such as a bank) comprising an existing customerdatabase to sequentially order and optimize outbound marketing events(herein referred to marketing events) for offering to groups ofcandidates from the existing customer database. The database system 2comprises database structures 9, 10, 12, 14, 15, and 18, and a databasemanager software application 19. The database manager softwareapplication 19 is a single database manager software application (i.e.,one software application as opposed to multiple software applications)comprising multiple software components. The database manager softwareapplication 19 may comprise any type of database management softwareapplication including, inter alia, DB2 database management system byIBM, etc. The database manager software application 19 comprises acomputing tool 6 and an optimization tool 8 (i.e., software components).Using a single database manager software application (i.e., databasemanager software application 19) comprising multiple software components(i.e., grouping tool 7, computing tool 6 and optimization tool 8) isadvantageous over using a plurality of individual database managersoftware applications (e.g., a first individual database managersoftware application comprising a computing tool, a second individualdatabase manager software application comprising an optimization tool,and a third individual database manager software application comprisinga grouping tool ) because communications between components of a singledatabase manager software application (e.g., grouping tool, 7, computingtool 6, optimization tool 8, etc.) are faster and more efficient thancommunications between multiple individual database manager softwareapplications. Communications between components of a single databasemanager software application are faster and more efficient thancommunications between multiple individual database manager softwareapplications because data (for manipulation) only has to be moved withina single software application and does not have to be moved from onesoftware application to another software application. Thereforefunctions performed on the data (e.g., calculations, optimizations, etc)are performed faster and more efficiently by a single database managersoftware application than by multiple database manager softwareapplications. Each of database structures 9, 10, 12, 14, 15, and 18 maybe individual database structures within the database system 2.Alternatively, each of database structures 9, 10, 12, 14, 15, and 18 maybe tables or sections within a single database system (e.g., asillustrated in FIG. 1). Database structure 9 comprises customer (i.e.,candidate) data including, inter alia, a list of candidates, data modelsusing any customer past history data (e.g., credit card balances, pastspending habits, etc.), etc. The customer (i.e., candidate) datacomprises information about all of the entity's customers, current orprospective. This data may be compiled from any standard sourceincluding, inter alia, an existing marketing database containing dataregarding active customers of the entity. The customer data primarilycomprises candidate traits for each candidate, behavioral data (e.g.,purchasing history, returned-items history, payment history, etc),promotional (i.e., marketing events) history (e.g., past marketingevents offered to a candidate including information regarding whichmarketing events were accepted by the candidate), and demographic dataregarding the candidate. A candidate trait is defined herein as a typeof characteristic that a candidate comprises. For example a candidatetrait may comprise, inter alia, a candidate income range (e.g., lowincome, high income, etc.), a geographical location, an age group, acredit rating, etc. Database structure 9 additionally comprises apredetermined total budget for each candidate regarding an amount ofmoney that the entity offering the marketing events is willing to spendto offer a plurality of marketing events to the candidate. Databasestructure 12 comprises the marketing offers. Database structure 12additionally comprises a predetermined value score associated with eachmarketing offer. A value score is a numerical score (e.g., in dollars)representing an expected profit gain to be produced by a marketing offerfrom a candidate accepting and executing the marketing offer. Themarketing offers are placed in groups according to a specific candidatetrait. For example, a first group of marketing offers may be grouped foroffering to candidates from a specific geographical location and asecond group of marketing offers may be grouped for offering tocandidates with a specific credit rating, etc. Database structure 12additionally comprises drop date data for each of the marketing offersand/or groups of marketing offers. A drop date is an origin date foroffering the marketing offers. Database structure 14 comprisesconstraint data regarding the marketing offer(s) as applied to thegroups of candidates. Constraint data is defined herein as constraintsthat are associated with offering a marketing offer to a candidate. Forexample, constraint data may include, inter alia, an amount of moneyregarding execution costs for offering the marketing offer to acandidate(s) (i.e., an amount that the entity has determined that theyare willing is to spend to offer the marketing event to thecandidate(s)), a maximum number of offerings for a marketing event(e.g., how many times a marketing event is offered to a candidate),timing between offers, etc. Execution costs comprise costs associatedwith using each of the channels in the channels database structure 18.Execution costs may comprise, inter alia, costs for promotionalmaterials, mailing costs, telemarketing costs, infrastructure costs,etc. Database structure 18 comprises data regarding channels that may beused to communicate the marketing offer to a group of candidates (e.g.,e-mail, direct mail, text message, telephone, etc.). The grouping tool 7divides candidates from the list of candidates in the database structure9 into groups. Each group of candidates is grouped by association with aspecific candidate trait. The grouping tool 7 then matches each group ofcandidates with an associated group of marketing offers associated witha same specific candidate trait. The matched groups of candidates andmarketing offers are placed in the database structure 10. The computingtool 6 applies the data from database structure 18 to the matched groupsof candidates and marketing offers from database structure 10 to createa plurality of matched groups of candidates and marketing events. Thecomputing tool 6 uses data supplied from the database structures 9 10,and 12 to compute response probability scores and ranking scores foreach of the marketing events within a group with respect to eachassociated group of candidates. The response probability scores are fordetermining a probability that candidates within a group will respond toeach of the marketing events. In other words, the response probabilityscores determine a probability that each candidate within a group willrespond to and accept a marketing event within a specified time frame.The ranking score is computed as a function of a value score withrespect to a response probability score (e.g., by multiplying a valuescore for a marketing event with a response probability score for agroup of candidates). Each ranking score is associated with a marketingevent within a group for candidates within an associated group. Theranking scores are used to order the marketing events (e.g., fromhighest rank to lowest rank) to determine an order in which to offer theplurality of marketing events to the associated group of candidates. Theoptimization tool 8 sorts the ranking scores (i.e., for the marketingevents) from highest rank to lowest rank for a group of candidates and aranking list is created. The optimization tool 8 uses data from thedatabase structure 14 to apply constraint data (e.g., timingconstraints, budget constraints, etc.) to each of the ranking scores onthe ranking list for the group of candidates. The optimization tool 8applies timing constraints to each of the ranking scores for each of themarketing events within a group. The timing constraints eliminate anymarketing events that comprise timing conflicts between marketingevents. For example, the optimization tool 8 will take the highestranked marketing event (first marketing event) and compare it to thenext highest ranked marketing event (second marketing event). If the twomarketing events comprise a same type of marketing event (e.g., bothmarketing events comprise a credit card offer), then the optimizationtool looks for a timing conflict. For example if the first marketingevent is to be offered to a group of candidates every 30 days and thesecond marketing event is to be offered every 30 days then the secondmarketing event is eliminated from the ranking list because the twomarketing events are same type of marketing event and should not bereceived by the group of candidates at the same time. The optimizationtool 8 will continue to apply the timing constraints to each of themarketing events on the ranking list. Marketing events comprising timingconflicts are eliminated from the ranking list. The optimization tool 8then applies monetary (i.e., budget) constraints to the value scoresthat remain on the ranking list and subtracts the monetary constraintsfrom the predetermined budgeted execution costs for offering themarketing event to the group of candidates. The predetermined budgetedamount comprises an amount of money for execution costs that the entityhas determined that they are willing is to spend to offer a plurality ofmarketing events to the group of candidates. For example, each time amarketing event is offered to a group of candidates, the execution costsare deducted from the budgeted amount, and once the budget is exceeded,the optimization tool 8 will eliminate any remaining marketing eventsfrom the ranking list. The final ranking list comprises marketing eventsthat have not been eliminated and a specified order for offering themarketing events to the associated group of candidates. The finalranking list is stored in the database structure 15.

Table 1 illustrates an example of sorted ranking scores with constraintdata applied and subtracted from the budget. TABLE 1 Ranking scoreConstraint data Budget $50 $10 $30 $40 $5 $20 $30 $10 $15 $20 $5 $5 $10$10 $0

The first row comprises the highest ranked marketing event ($50 rank).The total budget is $30 and the associated constraint data is $10. Theconstraint data ($10) is subtracted from the budget ($30) for thehighest ranked marketing event leaving $20 in the budget for offeringmore marketing events to the group of candidates. The second rowcomprises the next ranked marketing event ($40 rank). The constraintdata ($5) is subtracted from the budget ($20) for the next rankedmarketing event ($40 rank) leaving $15 in the budget offering moremarketing events to the group of candidates. The optimization tool goesthrough each ranked marketing event until there is no more money left inthe budget (see row 5) thereby eliminating any more offerings formarketing events. The first four rows comprise the marketing events tobe offered sequentially to the candidate. The fifth row comprises aneliminated marketing event due to an exhausted budget ($0).

An example of an implementation for the database system 2 of FIG. 1 fordynamically ordering marketing events for a group of candidates isdescribed as follows. This example comprises six marketing offers andfour channels.

Marketing Offers

-   1. A Mortgage Offer-   2. A Credit Card offer-   3. A Household insurance offer-   4. An Auto insurance offer-   5. A Platinum Credit Card-   6. A low rate loan offer    Channels-   1. Mail-   2. Email-   3. Outbound telephone call-   4. Text message

The 6 marketing offers are multiplied by the 4 channels to produce 24marketing events. Each marketing event comprises a drop date andtherefore a calendar of events. A first group of candidates is scoredfor each of the 24 marketing events with propensity to respond (i.e., aresponse probability score) to each of the marketing events. All 24response probability scores are calculated in parallel and each scorecomprises a range between 0 and 1 with 1 comprising the highestpropensity to respond to a marketing event and 0 comprising the lowestpropensity to respond to a marketing event. Each of the marketing eventscomprises an expected profit gain (i.e., value score). For example, ifthe marketing offer is a mortgage offer, the expected profit margin(i.e., value score) may be calculated based on an annual return ofrepayments vs. infrastructure costs balanced against the risk of thecandidate defaulting on the mortgage vs. prepayment of mortgage beforethe term is up (although the mortgage may be loaded with a prepaymentpenalty clause to protect a revenue stream). A ranking score for each ofthe 24 marketing events is calculated as a function of a value scorewith respect to a response probability score for the associatedmarketing event with respect to the group of candidates (e.g., bymultiplying each value score for each marketing event by a responseprobability score for the associated marketing event with respect to thegroup of candidates). The aforementioned process is performed by acomputing tool (e.g., computing tool 6 in FIG. 1). An optimization tool(e.g., optimization tool 8 in FIG. 1) sorts the 24 marketing events fromhighest ranking scores to lowest ranking scores. The optimization toolmay alternatively sort 24 marketing events in any manner. Theoptimization tool applies constraint data including timing constraintsand monetary constraints (i.e., verses budget) to the 24 marketingevents. The constraint data is applied to the 24 marketing eventsstarting with the highest ranked marketing event to the lowest rankingmarketing event and ultimately an optimized execution list is producedcomprising a stream of marketing events that the group of candidateswill receive. As an alternative, the marketing events may be ordered andoptimized for the group of candidates by the optimization tool 8 withoutusing value scores, response probability scores, and ranking scores.Additionally, the marketing events may be ordered and optimized by theoptimization tool 8 for the group of candidates using any combination ofthe value scores, response probability scores, and ranking scores.

FIG. 2 illustrates a flowchart comprising an algorithm used by databasesystem 2 of FIG. 1 for dynamically ordering a plurality of marketingevents for offering to a group of candidates, in accordance withembodiments of the present invention. In step 21, the grouping tool 7places the candidates from a list in groups according to specificcandidate traits. In step 22, the grouping tool 7 matches each group ofcandidates with an associated group of marketing offers associated witha same specific candidate trait. The matched groups of candidates andmarketing offers are placed in the database structure 10. In step 23,the computing tool 6 retrieves data from the database structure 10 tocompute response probability scores and ranking scores for each of themarketing events within a group with respect to an associated group ofcandidates. In step 24, ranking scores for each marketing event withinthe group are calculated by the computing tool 6. In step 25, theoptimization tool 8 sorts the ranking scores (i.e., for the marketingevents) from highest rank to lowest rank for the group of candidates anda ranking list is created. In step 26, the optimization tool 8 appliestiming constraints to each of the ranking scores within the group. Thetiming constraints eliminate any marketing events that comprise timingconflicts. In step 28, the optimization tool 8 applies monetaryconstraints to the value scores that remain after the timing constraintshave been applied and subtracts the monetary constraints from thepredetermined budgeted execution costs for offering the marketing eventto the group of candidates. In step 30, a final ranking list is createdas a result of execution steps 26 and 28. Note that steps 26 and 28 maybe performed in any order. The final ranking list comprises marketingevents that have not been eliminated (i.e., by timing and monetaryconstraints). The final ranking list comprises a specified order foroffering the marketing events to the group of candidates. Ranking aplurality of marketing events for the group of candidates may be done inparallel. Additionally, ranking a plurality of marketing events for aplurality of groups of candidates may be done in simultaneously inparallel.

FIG. 3 illustrates a computer system 90 used for implementing thedatabase system of FIG. 1 for dynamically ordering a plurality ofmarketing events for offering to a group of candidates, in accordancewith embodiments of the present invention. The computer system 90comprises a processor 91, an input device 92 coupled to the processor91, an output device 93 coupled to the processor 91, and memory devices94 and 95 each coupled to the processor 91. The input device 92 may be,inter alia, a keyboard, a mouse, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes an algorithm for dynamically ordering a plurality ofmarketing events for offering to a group of candidates. The processor 91executes the computer code 97. The memory device 94 includes input data96. The input data 96 includes input required by the computer code 97.The output device 93 displays output from the computer code 97. Eitheror both memory devices 94 and 95 (or one or more additional memorydevices not shown in FIG. 3) may comprise the database system 2 of FIG.1 and may be used as a computer usable medium (or a computer readablemedium or a program storage device) having a computer readable programcode embodied therein and/or having other data stored therein, whereinthe computer readable program code comprises the computer code 97.Generally, a computer program product (or, alternatively, an article ofmanufacture) of the computer system 90 may comprise said computer usablemedium (or said program storage device).

Thus the present invention discloses a process for deploying orintegrating computing infrastructure, comprising integratingcomputer-readable code into the computer system 90, wherein the code incombination with the computer system 90 is capable of performing amethod used for dynamically ordering a plurality of marketing events foroffering to a group of candidates.

While FIG. 3 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 3. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

1. A database system, comprising: a first database structure storing afirst list identifying marketing events, wherein each marketing eventfrom said first list comprises a marketing offer and an identifiedchannel means for communicating said marketing offer, and wherein saidmarketing events from said first list are divided into a first pluralityof groups; a second database structure storing a second list ofcandidates; and a database manager software application stored on acomputer readable medium, wherein said database manager softwareapplication comprises a grouping tool and an optimization tool, whereinsaid grouping tool is for dividing candidates from said second list ofcandidates into a second plurality of groups and matching a first groupfrom said first plurality of groups with a second group from said secondplurality of groups, wherein candidates from said second group comprisea first specified candidate trait, and wherein said optimization tool isfor optimizing and sorting, said marketing events from said first groupfor all candidates from said second group.
 2. The database system ofclaim 1, wherein said first database structure further stores a thirdlist of value scores, wherein each value score from said third list isassociated with a marketing event from said first list, wherein saiddatabase manager software application further comprises a computing toolfor computing a ranking score for each of said marketing events fromsaid first group for all of said candidates from said second group,wherein each of said ranking scores are computed as a function of avalue score from said third list with respect to it's associatedmarketing event from said first group for said candidates from saidsecond group, and wherein said marketing events from said first groupare optimized and sorted for all of said candidates from said secondgroup by optimizing and sorting said ranking scores for each of saidmarketing events from said from said first group.
 3. The database systemof claim 2, further comprising a third database structure storing atleast one data model, wherein said computing tool is for computingresponse probability scores for said marketing events from said firstgroup for all of said candidates within said second group, wherein eachof said response probability scores are computed using said at least onedata model, and wherein each of said ranking scores are computed as afunction said value score from said third list with respect to it'sassociated marketing event from said first group for said candidatesfrom said second group and a response probability score from saidresponse probability scores for said associated marketing event.
 4. Thedatabase system of claim 1, wherein said grouping tool is for matching athird group from said first plurality of groups with a fourth group fromsaid second plurality of groups, wherein all candidates from said fourthgroup comprise a second specified candidate trait, wherein said firstspecified candidate trait comprises a different candidate trait thansaid second specified candidate trait, and wherein said optimizationtool is for optimizing and sorting, said marketing events from saidthird group for all candidates from said fourth group.
 5. The databasesystem of claim 1, wherein said optimization tool is for generating apriority list comprising said sorted marketing events from said firstgroup, and wherein said priority list prioritizes an order in which tooffer each of said marketing events from said first group to all of saidcandidates from said second group.
 6. The selection system of claim 1,wherein said optimization tool is for generating a priority listcomprising a first set of marketing events from said sorted marketingevents from said first group that fall within a specified set ofconstraints, and wherein said priority list is for prioritizing an orderin which to offer each of said marketing events from said first group toall of said candidates from said second group.
 7. The database system ofclaim 6, wherein said specified set of constraints comprise timingconflicts related to offering all marketing events within said firstgroup.
 8. The database system of claim 6, wherein said specified set ofconstraints comprise budget constraints related to offering allmarketing events within said first group.
 9. The database system ofclaim 1, wherein said list of candidates comprises existing customers ofan entity that is offering said plurality of marketing events.
 10. Thedatabase system of claim 1, wherein said marketing events from saidfirst group are sorted and optimized essentially simultaneously.
 11. Thedatabase system of claim 1, wherein each marketing offer is selectedfrom the group consisting of a product offer and a service offer. 12.The database system of claim 1, wherein each identified channel means isselected from the group consisting of a telephone call, an email, a textmessage, and standard mail.
 13. A selection method, comprising:providing a database system comprising a first database structurestoring a first list identifying marketing events, a second databasestructure storing a second list of candidates, and a database managersoftware application stored on a computer readable medium, wherein saiddatabase manager software application comprises a grouping tool and anoptimization tool, wherein each marketing event from said first listcomprises a marketing offer and an identified channel means forcommunicating said marketing offer, and wherein said marketing eventsfrom said first list are divided into a first plurality of groups;dividing by said grouping tool, candidates from said second list ofcandidates into a second plurality of groups; matching by said groupingtool, a first group from said first plurality of groups with a secondgroup from said second plurality of groups, wherein all candidates fromsaid second group comprise a first specified candidate trait;optimizing, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group; andsorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.
 14. Themethod of claim 13, wherein said first database structure further storesa third list of value scores, wherein each value score from said thirdlist is associated with a marketing event from said first list, whereinsaid database manager software application further comprises a computingtool, and wherein said method further comprises: computing by saidcomputing tool, a ranking score for each of said marketing events fromsaid first group for all of said candidates from said second group,wherein each of said ranking scores are computed as a function of avalue score from said third list with respect to it's associatedmarketing event from said first group for said candidates from saidsecond group, and wherein said marketing events from said first groupare optimized and sorted for all of said candidates from said secondgroup by optimizing and sorting said ranking scores for each of saidmarketing events from said from said first group.
 15. The method ofclaim 14, further comprising: providing a third database structurestoring at least one data model; and computing by said computing tool,response probability scores for said marketing events from said firstgroup for all of said candidates within said second group, wherein eachof said response probability scores are computed using said at least onedata model, and wherein each of said ranking scores are computed as afunction said value score from said third list with respect to it'sassociated marketing event from said first group for said candidatesfrom said second group and a response probability score from saidresponse probability scores for said associated marketing event.
 16. Themethod of claim 13 further comprising: matching by said grouping tool, athird group from said first plurality of groups with a fourth group fromsaid second plurality of groups, wherein all candidates from said fourthgroup comprise a second specified candidate trait, wherein said firstspecified candidate trait comprises a different candidate trait thansaid second specified candidate trait; optimizing, by said optimizationtool, said marketing events from said third group for all candidatesfrom said fourth group; and sorting, by said optimization tool, saidmarketing events from said third group for all of said candidates fromsaid fourth group.
 17. The method of claim 13, further comprisinggenerating by the optimization tool, a priority list comprising saidsorted marketing events from said first group, and wherein said prioritylist prioritizes an order in which to offer each of said marketingevents from said first group to all of said candidates from said secondgroup.
 18. The method of claim 13, further comprising generating by theoptimization tool, a priority list comprising a first set of marketingevents from said sorted marketing events from said first group that fallwithin a specified set of constraints, and wherein said priority list isfor prioritizing an order in which to offer each of said marketingevents from said first group to all of said candidates from said secondgroup.
 19. The method of claim 18, wherein said specified set ofconstraints comprise timing conflicts related to offering all of saidmarketing events from said first group.
 20. The database system of claim18, wherein said specified set of constraints comprise budgetconstraints related to offering all of said marketing events from saidfirst group.
 21. The method of claim 13, wherein said list of candidatescomprises existing customers of an entity that is offering saidplurality of marketing events.
 22. The method of claim 13, wherein saidmarketing events from said first group are sorted and optimizedessentially simultaneously.
 23. The method of claim 13, wherein eachmarketing offer is selected from the group consisting of a product offerand a service offer.
 24. The method of claim 13, wherein each identifiedchannel means is selected from the group consisting of a telephone call,an email, a text message, and standard mail.
 25. A process forintegrating computing infrastructure, comprising integratingcomputer-readable code into a computing system, wherein the code incombination with the computing system comprises a database systemcomprising a first database structure storing a first list identifyingmarketing events, a second database structure storing a second list ofcandidates, and a database manager software application stored on acomputer readable medium, wherein said database manager softwareapplication comprises a grouping tool and an optimization tool, whereineach marketing event from said first list comprises a marketing offerand an identified channel means for communicating said marketing offer,wherein said marketing events from said first list are divided into afirst plurality of groups, and wherein the code in combination with thecomputing system is adapted to implement a method for performing thesteps of: dividing by said grouping tool, candidates from said secondlist of candidates into a second plurality of groups; matching by saidgrouping tool, a first group from said first plurality of groups with asecond group from said second plurality of groups, wherein allcandidates from said second group comprise a first specified candidatetrait; optimizing, by said optimization tool, said marketing events fromsaid first group for all of said candidates from said second group; andsorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.
 26. Theprocess of claim 25, wherein said first database structure furtherstores a third list of value scores, wherein each value score from saidthird list is associated with a marketing event from said first list,wherein said database manager software application further comprises acomputing tool, and wherein said method further comprises: computing bysaid computing tool, a ranking score for each of said marketing eventsfrom said first group for all of said candidates from said second group,wherein each of said ranking scores are computed as a function of avalue score from said third list with respect to it's associatedmarketing event from said first group for said candidates from saidsecond group, and wherein said marketing events from said first groupare optimized and sorted for all of said candidates from said secondgroup by optimizing and sorting said ranking scores for each of saidmarketing events from said from said first group.
 27. The process ofclaim 26, further comprising providing a third database structurestoring at least one data model, wherein said method further comprises:computing by said computing tool, response probability scores for saidmarketing events from said first group for all of said candidates withinsaid second group, wherein each of said response probability scores arecomputed using said at least one data model, and wherein each of saidranking scores are computed as a function said value score from saidthird list with respect to it's associated marketing event from saidfirst group for said candidates from said second group and a responseprobability score from said response probability scores for saidassociated marketing event.
 28. The process of claim 25, wherein saidmethod further comprises: matching by said grouping tool, a third groupfrom said first plurality of groups with a fourth group from said secondplurality of groups, wherein all candidates from said fourth groupcomprise a second specified candidate trait, wherein said firstspecified candidate trait comprises a different candidate trait thansaid second specified candidate trait; optimizing, by said optimizationtool, said marketing events from said third group for all candidatesfrom said fourth group; and sorting, by said optimization tool, saidmarketing events from said third group for all of said candidates fromsaid fourth group.
 29. The process of claim 25, wherein said methodfurther comprises generating by the optimization tool, a priority listcomprising said sorted marketing events from said first group, andwherein said priority list prioritizes an order in which to offer eachof said marketing events from said first group to all of said candidatesfrom said second group.
 30. The process of claim 25, wherein said methodfurther comprises generating by the optimization tool, a priority listcomprising a first set of marketing events from said sorted marketingevents from said first group that fall within a specified set ofconstraints, and wherein said priority list is for prioritizing an orderin which to offer each of said marketing events from said first group toall of said candidates from said second group.
 31. The process of claim30, wherein said specified set of constraints comprise timing conflictsrelated to offering all of said marketing events within said firstgroup.
 32. The process of claim 30, wherein said specified set ofconstraints comprise budget constraints related to offering all of saidmarketing events within said first group.
 33. The process of claim 25,wherein said list of candidates comprises existing customers of anentity that is offering said plurality of marketing events.
 34. Theprocess of claim 25, wherein said marketing events from said first groupare sorted and optimized essentially simultaneously.
 35. The process ofclaim 25, wherein each marketing offer is selected from the groupconsisting of a product offer and a service offer.
 36. The process ofclaim 25, wherein each identified channel means is selected from thegroup consisting of a telephone call, an email, a text message, andstandard mail.
 37. A computer program product, comprising a computerusable medium having a computer readable program code embodied therein,said computer readable program code comprising an algorithm adapted toimplement a method for ordering a first list identifying of marketingevents within a database system, said database system comprising adatabase system comprising a first database structure storing said firstlist identifying marketing events, a second database structure storing asecond list of candidates, and a database manager software applicationstored on a computer readable medium, wherein said database managersoftware application comprises a grouping tool and an optimization tool,wherein each marketing event from said first list comprises a marketingoffer and an identified channel means for communicating said marketingoffer, wherein said marketing events from said first list are dividedinto a first plurality of groups, said method comprising the steps of:dividing by said grouping tool, candidates from said second list ofcandidates into a second plurality of groups; matching by said groupingtool, a first group from said first plurality of groups with a secondgroup from said second plurality of groups, wherein all candidates fromsaid second group comprise a first specified candidate trait;optimizing, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group; andsorting, by said optimization tool, said marketing events from saidfirst group for all of said candidates from said second group.
 38. Thecomputer program product of claim 37, wherein said first databasestructure further stores a third list of value scores, wherein eachvalue score from said third list is associated with a marketing eventfrom said first list, wherein said database manager software applicationfurther comprises a computing tool, and wherein said method furthercomprises: computing by said computing tool, a ranking score for each ofsaid marketing events from said first group for all of said candidatesfrom said second group, wherein each of said ranking scores are computedas a function of a value score from said third list with respect to it'sassociated marketing event from said first group for said candidatesfrom said second group, and wherein said marketing events from saidfirst group are optimized and sorted for all of said candidates fromsaid second group by optimizing and sorting said ranking scores for eachof said marketing events from said from said first group.
 39. Thecomputer program product of claim 38, further comprising providing athird database structure storing at least one data model, wherein saidmethod further comprises: computing by said computing tool, responseprobability scores for said marketing events from said first group forall of said candidates within said second group, wherein each of saidresponse probability scores are computed using said at least one datamodel, and wherein each of said ranking scores are computed as afunction said value score from said third list with respect to it'sassociated marketing event from said first group for said candidatesfrom said second group and a response probability score from saidresponse probability scores for said associated marketing event.
 40. Thecomputer program product of claim 37, wherein said method furthercomprises: matching by said grouping tool, a third group from said firstplurality of groups with a fourth group from said second plurality ofgroups, wherein all candidates from said fourth group comprise a secondspecified candidate trait, wherein said first specified candidate traitcomprises a different candidate trait than said second specifiedcandidate trait; optimizing, by said optimization tool, said marketingevents from said third group for all candidates from said fourth group;and sorting, by said optimization tool, said marketing events from saidthird group for all of said candidates from said fourth group.
 41. Thecomputer program product of claim 37, wherein said method furthercomprises generating by the optimization tool, a priority listcomprising said sorted marketing events from said first group, andwherein said priority list prioritizes an order in which to offer eachof said marketing events from said first group to all of said candidatesfrom said second group.
 42. The computer program product of claim 37,wherein said method further comprises generating by the optimizationtool, a priority list comprising a first set of marketing events fromsaid sorted marketing events from said first group that fall within aspecified set of constraints, and wherein said priority list is forprioritizing an order in which to offer each of said marketing eventsfrom said first group to all of said candidates from said second group.43. The computer program product of claim 42 wherein said specified setof constraints comprise timing conflicts related to offering all of saidmarketing events within said first group.
 44. The computer programproduct of claim 42, wherein said specified set of constraints comprisebudget constraints related to offering all of said marketing eventswithin said first group.
 45. The computer program product of claim 37,wherein said list of candidates comprises existing customers of anentity that is offering said plurality of marketing events.
 46. Thecomputer program product of claim 37, wherein said marketing events fromsaid first group are sorted and optimized essentially simultaneously.47. The computer program product of claim 37, wherein each marketingoffer is selected from the group consisting of a product offer and aservice offer.
 48. The computer program product of claim 37, whereineach identified channel means is selected from the group consisting of atelephone call, an email, a text message, and standard mail.